Google's Business Model
Google’s Hybrid Approach to Research
Alfred Spector Google Inc. azs@google.com
Peter Norvig Google Inc. pnorvig@google.com
Slav Petrov Google Inc. slav@google.com
1
Introduction
In this paper, we describe how we organize Computer Science (CS) research at Google. We focus on how we integrate research and development (R&D) and discuss the benefits and risks of our approach. The challenge in organizing R&D is great because CS is an increasingly broad and diverse field. It combines aspects of mathematical reasoning, engineering methodology, and the empirical approaches of the scientific method. The empirical components are clearly on the upswing, in part because the computer systems we construct have become so large that analytic techniques cannot properly describe their properties, because the systems now dynamically adjust to the hard-to-predict needs of a diverse user community, and because the systems can learn from vast data sets and large numbers of interactive sessions that provide continuous feedback. We have also noted that CS is an expanding sphere, where the core of the field (Theory, Operating Systems, etc.) continues to grow in depth, while the field keeps expanding into neighboring application areas. Research results come not only from universities, but also from companies, large and small. The way that research results are disseminated is also evolving and the peer-reviewed paper is under threat as the dominant dissemination method. Open source releases, standards specifications, data releases, and novel commercial systems that set new standards upon which others then build, are increasingly important. To compare our approach to research with that of other companies is beyond the scope of this paper. But, for reference, we note that in the terminology of Pasteur’s Quadrant [1], we do “use-inspired basic” and “pure applied” (CS) research. [2] and [3] discuss information technology research generally, pointing out the movement in industrial
Alfred Spector Google Inc. azs@google.com
Peter Norvig Google Inc. pnorvig@google.com
Slav Petrov Google Inc. slav@google.com
1
Introduction
In this paper, we describe how we organize Computer Science (CS) research at Google. We focus on how we integrate research and development (R&D) and discuss the benefits and risks of our approach. The challenge in organizing R&D is great because CS is an increasingly broad and diverse field. It combines aspects of mathematical reasoning, engineering methodology, and the empirical approaches of the scientific method. The empirical components are clearly on the upswing, in part because the computer systems we construct have become so large that analytic techniques cannot properly describe their properties, because the systems now dynamically adjust to the hard-to-predict needs of a diverse user community, and because the systems can learn from vast data sets and large numbers of interactive sessions that provide continuous feedback. We have also noted that CS is an expanding sphere, where the core of the field (Theory, Operating Systems, etc.) continues to grow in depth, while the field keeps expanding into neighboring application areas. Research results come not only from universities, but also from companies, large and small. The way that research results are disseminated is also evolving and the peer-reviewed paper is under threat as the dominant dissemination method. Open source releases, standards specifications, data releases, and novel commercial systems that set new standards upon which others then build, are increasingly important. To compare our approach to research with that of other companies is beyond the scope of this paper. But, for reference, we note that in the terminology of Pasteur’s Quadrant [1], we do “use-inspired basic” and “pure applied” (CS) research. [2] and [3] discuss information technology research generally, pointing out the movement in industrial
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